MovieNethttp://movienet.site/ is a holistic dataset for movie understanding, which contains 1,100 movies with a large amount of data, e.g., movie frames, trailers, plot descriptions, etc.. It also contains rich annotations, including the more than 1.1M characters with millions of bounding boxes and 3,087 cast identities. On MovieNet[1]}, Huang et al.established benchmarks for many tasks based on movie data, including the character identification. They crop the characters with ground-truth boxes and combine the face and body features for the identification. However, simultaneously detecting the characters and identifying them is much closer to realistic application scenarios. Therefore, we extend the character identification task to character search. In order to make the experiments easy to execute and reproduce, we split out the data and annotations which are useful for the character search task, and reorganized the dataset. The character search dataset is split into the training and test sets based on the 3,087 identities. 1,000 identities of them are included in the test set, while the rest 2,087 form the training set. However, considering the extremely large scale of the whole 100K training images, we define 3 different settings for the training sets. Specifically, we adopt at most 10, 30, and 70 instances per identity, contributing to the training sets of 20K, 54K, and 100K training images, respectively. We follow CUHK-SYSU [2]} to introduce different gallery sizes to bring various levels of difficulty for comprehensive evaluation. We denote the dataset with our proposed data splits and settings as MovieNet-CS.
